EKS Cluster w/ NVIDIA GPUs and EFA for Machine Learning¶
This pattern demonstrates an Amazon EKS Cluster with an EFA-enabled nodegroup that utilizes p5.48xlarge instances with H100 NVIDIA GPUs used in distributed, multi-node machine learning.
The following components are demonstrated in this pattern:
- A "default" node group that supports addons and components that do not require GPUs nor EFA devices. Any pods that do not tolerate the taints of the GPU node group will be scheduled on instances within this node group.
- A node group of
p5.48xlargeinstances with:- all x32 EFA network interfaces enabled
- provisioned within a placement group so that the instances are provisioned close to one another in a single availability zone that supports the instance type
- a common NVIDIA taint of
"nvidia.com/gpu:NoSchedule"to ensure only the intended applications are allowed to run on the nodes created - two labels to identify that this nodegroup supports NVIDIA GPUs and EFA devices and allow pods to use node selectors with these labels
- the NVME instance store volumes are mounted in a RAID-0 array to provide a single, large, high-performance storage volume for the GPU workloads
- kubelet and containerd are configured to utilize the RAID-0 volume, allowing kubelet to discover the additional storage as ephemeral storage that can be utilized by pods
- A Helm chart deployment for the NVIDIA device plugin to expose and mount the GPUs provided by the instances to the pods that request them
- A Helm chart deployment for the EFA device plugin to expose and mount the EFA network interfaces provided by the instances to the pods that request them. Since the EFA network interfaces are only found on the instances that provide NVIDIA GPUs in this pattern, we do not apply an additional taint for the EFA network interfaces to avoid over-constraining.
Code¶
Cluster¶
################################################################################
# Cluster
################################################################################
module "eks" {
source = "terraform-aws-modules/eks/aws"
version = "~> 20.26"
cluster_name = local.name
cluster_version = "1.31"
# Give the Terraform identity admin access to the cluster
# which will allow it to deploy resources into the cluster
enable_cluster_creator_admin_permissions = true
cluster_endpoint_public_access = true
cluster_addons = {
coredns = {}
eks-pod-identity-agent = {}
kube-proxy = {}
vpc-cni = {
most_recent = true
}
}
# Add security group rules on the node group security group to
# allow EFA traffic
enable_efa_support = true
vpc_id = module.vpc.vpc_id
subnet_ids = module.vpc.private_subnets
eks_managed_node_groups = {
nvidia-efa = {
# The EKS AL2023 NVIDIA AMI provides all of the necessary components
# for accelerated workloads w/ EFA
ami_type = "AL2023_x86_64_NVIDIA"
instance_types = ["p5.48xlarge"]
# Mount instance store volumes in RAID-0 for kubelet and containerd
# https://github.com/awslabs/amazon-eks-ami/blob/master/doc/USER_GUIDE.md#raid-0-for-kubelet-and-containerd-raid0
cloudinit_pre_nodeadm = [
{
content_type = "application/node.eks.aws"
content = <<-EOT
---
apiVersion: node.eks.aws/v1alpha1
kind: NodeConfig
spec:
instance:
localStorage:
strategy: RAID0
EOT
}
]
min_size = 2
max_size = 2
desired_size = 2
# This will:
# 1. Create a placement group to place the instances close to one another
# 2. Ignore subnets that reside in AZs that do not support the instance type
# 3. Expose all of the available EFA interfaces on the launch template
enable_efa_support = true
labels = {
"vpc.amazonaws.com/efa.present" = "true"
"nvidia.com/gpu.present" = "true"
}
taints = {
# Ensure only GPU workloads are scheduled on this node group
gpu = {
key = "nvidia.com/gpu"
value = "true"
effect = "NO_SCHEDULE"
}
}
}
# This node group is for core addons such as CoreDNS
default = {
instance_types = ["m5.large"]
min_size = 1
max_size = 2
desired_size = 2
}
}
tags = local.tags
}
Device Plugins¶
################################################################################
# Helm charts
################################################################################
resource "helm_release" "nvidia_device_plugin" {
name = "nvidia-device-plugin"
repository = "https://nvidia.github.io/k8s-device-plugin"
chart = "nvidia-device-plugin"
version = "0.16.2"
namespace = "nvidia-device-plugin"
create_namespace = true
wait = false
}
resource "helm_release" "aws_efa_device_plugin" {
name = "aws-efa-k8s-device-plugin"
repository = "https://aws.github.io/eks-charts"
chart = "aws-efa-k8s-device-plugin"
version = "v0.5.5"
namespace = "kube-system"
wait = false
values = [
<<-EOT
nodeSelector:
vpc.amazonaws.com/efa.present: 'true'
tolerations:
- key: nvidia.com/gpu
operator: Exists
effect: NoSchedule
EOT
]
}
Deploy¶
See here for the prerequisites and steps to deploy this pattern.
Validate¶
Note
Desired instance type can be specified in eks.tf.
Values shown below will change based on the instance type selected (i.e. - p5.48xlarge has 8 GPUs and 32 EFA interfaces).
A list of EFA-enabled instance types is available here.
If you are using an on-demand capacity reservation (ODCR) for your instance type, please uncomment the capacity_reservation_specification block in eks.tf
and specify a capacity_reservation_id. Please ensure that the region and availability zone of your ODCR match the ones used in main.tf.
-
List the nodes and their instance type:
NAME STATUS ROLES AGE VERSION INSTANCE-TYPE ip-10-0-1-16.us-east-2.compute.internal Ready <none> 12h v1.29.3-eks-ae9a62a p5.48xlarge ip-10-0-12-113.us-east-2.compute.internal Ready <none> 14h v1.29.3-eks-ae9a62a m5.large ip-10-0-12-201.us-east-2.compute.internal Ready <none> 12h v1.29.3-eks-ae9a62a p5.48xlarge ip-10-0-46-217.us-east-2.compute.internal Ready <none> 14h v1.29.3-eks-ae9a62a m5.largeYou should see two EFA-enabled (in this example
p5.48xlarge) nodes in the list. -
Deploy Kubeflow MPI Operator
Kubeflow MPI Operator is required for running MPIJobs on EKS. We will use an MPIJob to test EFA. To deploy the MPI operator execute the following:
kubectl apply --server-side -f https://raw.githubusercontent.com/kubeflow/mpi-operator/v0.6.0/deploy/v2beta1/mpi-operator.yamlnamespace/mpi-operator serverside-applied customresourcedefinition.apiextensions.k8s.io/mpijobs.kubeflow.org serverside-applied serviceaccount/mpi-operator serverside-applied clusterrole.rbac.authorization.k8s.io/kubeflow-mpijobs-admin serverside-applied clusterrole.rbac.authorization.k8s.io/kubeflow-mpijobs-edit serverside-applied clusterrole.rbac.authorization.k8s.io/kubeflow-mpijobs-view serverside-applied clusterrole.rbac.authorization.k8s.io/mpi-operator serverside-applied clusterrolebinding.rbac.authorization.k8s.io/mpi-operator serverside-applied deployment.apps/mpi-operator serverside-applied -
EFA info test
This test prints a list of available EFA interfaces by using the
/opt/amazon/efa/bin/fi_infoutility. The script generate-efa-info-test.sh creates an MPIJob manifest file namedefa-info-test.yaml. It assumes that there are two cluster nodes with 8 GPU's per node and 32 EFA adapters. If you are not usingp5.48xlargeinstances in your cluster, you may adjust the settings in the script prior to running it.NUM_WORKERS- number of nodes you want to run the test onGPU_PER_WORKER- number of GPUs available on each nodeEFA_PER_WORKER- number of EFA interfaces available on each nodeTo start the test apply the generated manifest to the cluster:
Observe the pods in the current namespace. You should see a launcher pod and worker pods. It is normal for the launcher pod to restart a few times until the worker pods are fully running.
NAME READY STATUS RESTARTS AGE efa-info-test-launcher-wm8pm 0/1 CrashLoopBackOff 1 (16s ago) 19s efa-info-test-worker-0 1/1 Running 0 19s efa-info-test-worker-1 1/1 Running 0 19sNAME READY STATUS RESTARTS AGE efa-info-test-launcher-wm8pm 1/1 Running 2 (18s ago) 21s efa-info-test-worker-0 1/1 Running 0 21s efa-info-test-worker-1 1/1 Running 0 21sOnce the test launcher pod enters status
RunningorCompleted, see the test logs using the command below:Warning: Permanently added 'efa-info-test-worker-1.efa-info-test.default.svc' (ED25519) to the list of known hosts. Warning: Permanently added 'efa-info-test-worker-0.efa-info-test.default.svc' (ED25519) to the list of known hosts. [1,1]<stdout>:provider: efa [1,1]<stdout>: fabric: efa [1,1]<stdout>: domain: rdmap79s0-rdm [1,1]<stdout>: version: 120.10 [1,1]<stdout>: type: FI_EP_RDM [1,1]<stdout>: protocol: FI_PROTO_EFA ... [1,0]<stdout>:provider: efa [1,0]<stdout>: fabric: efa [1,0]<stdout>: domain: rdmap201s0-rdm [1,0]<stdout>: version: 120.10 [1,0]<stdout>: type: FI_EP_RDM [1,0]<stdout>: protocol: FI_PROTO_EFAFinally, remove the job:
-
EFA NCCL test
The EFA NCCL test is used to measure network bandwidth by running the
/opt/nccl-tests/build/all_reduce_perfutility. Create an MPIjob manifest by executing the script below:This script creates a file named
efa-nccl-test.yaml. Apply the manifest to start the EFA nccl test.kubectl apply --server-side -f ./efa-nccl-test.yaml ```text mpijob.kubeflow.org/efa-nccl-test createdSimilarly to the EFA info test, a launcher and worker pods will be created. The launcher pod will be in CrashLoopBackoff mode until the worker pods enter Running state. As soon as the launcher pod enters Running state as well, execute the following command to see the test logs:
... [1,0]<stdout>:# out-of-place in-place [1,0]<stdout>:# size count type redop root time algbw busbw #wrong time algbw busbw #wrong [1,0]<stdout>:# (B) (elements) (us) (GB/s) (GB/s) (us) (GB/s) (GB/s) [1,0]<stdout>: 8 2 float sum -1 87.96 0.00 0.00 0 78.05 0.00 0.00 0 [1,0]<stdout>: 16 4 float sum -1 76.83 0.00 0.00 0 77.15 0.00 0.00 0 [1,0]<stdout>: 32 8 float sum -1 77.37 0.00 0.00 0 75.38 0.00 0.00 0 [1,0]<stdout>: 64 16 float sum -1 77.60 0.00 0.00 0 79.80 0.00 0.00 0 [1,0]<stdout>: 128 32 float sum -1 77.20 0.00 0.00 0 77.78 0.00 0.00 0 [1,0]<stdout>: 256 64 float sum -1 78.46 0.00 0.01 0 80.39 0.00 0.01 0 [1,0]<stdout>: 512 128 float sum -1 77.56 0.01 0.01 0 78.00 0.01 0.01 0 [1,0]<stdout>: 1024 256 float sum -1 76.98 0.01 0.02 0 78.52 0.01 0.02 0 [1,0]<stdout>: 2048 512 float sum -1 77.92 0.03 0.05 0 78.64 0.03 0.05 0 [1,0]<stdout>: 4096 1024 float sum -1 83.26 0.05 0.09 0 83.16 0.05 0.09 0 [1,0]<stdout>: 8192 2048 float sum -1 88.46 0.09 0.17 0 86.32 0.09 0.18 0 [1,0]<stdout>: 16384 4096 float sum -1 97.22 0.17 0.32 0 94.82 0.17 0.32 0 [1,0]<stdout>: 32768 8192 float sum -1 98.84 0.33 0.62 0 99.85 0.33 0.62 0 [1,0]<stdout>: 65536 16384 float sum -1 101.1 0.65 1.22 0 96.80 0.68 1.27 0 [1,0]<stdout>: 131072 32768 float sum -1 100.5 1.30 2.44 0 99.13 1.32 2.48 0 [1,0]<stdout>: 262144 65536 float sum -1 104.5 2.51 4.70 0 102.2 2.57 4.81 0 [1,0]<stdout>: 524288 131072 float sum -1 108.8 4.82 9.04 0 109.8 4.78 8.96 0 [1,0]<stdout>: 1048576 262144 float sum -1 119.1 8.81 16.51 0 121.5 8.63 16.18 0 [1,0]<stdout>: 2097152 524288 float sum -1 145.8 14.39 26.97 0 144.7 14.49 27.17 0 [1,0]<stdout>: 4194304 1048576 float sum -1 163.2 25.70 48.19 0 162.4 25.82 48.42 0 [1,0]<stdout>: 8388608 2097152 float sum -1 197.9 42.38 79.46 0 197.9 42.39 79.48 0 [1,0]<stdout>: 16777216 4194304 float sum -1 282.3 59.43 111.43 0 290.3 57.79 108.35 0 [1,0]<stdout>: 33554432 8388608 float sum -1 442.8 75.77 142.07 0 417.4 80.39 150.73 0 [1,0]<stdout>: 67108864 16777216 float sum -1 597.4 112.34 210.64 0 591.6 113.43 212.68 0 [1,0]<stdout>: 134217728 33554432 float sum -1 872.0 153.92 288.60 0 872.8 153.78 288.34 0 [1,0]<stdout>: 268435456 67108864 float sum -1 1501.2 178.81 335.27 0 1503.1 178.59 334.86 0 [1,0]<stdout>: 536870912 134217728 float sum -1 2599.4 206.54 387.26 0 2553.0 210.29 394.29 0 [1,0]<stdout>: 1073741824 268435456 float sum -1 4556.5 235.65 441.85 0 5274.0 203.59 381.74 0 [1,0]<stdout>: 2147483648 536870912 float sum -1 9079.2 236.53 443.49 0 9128.1 235.26 441.11 0 [1,0]<stdout>: 4294967296 1073741824 float sum -1 17320 247.98 464.96 0 17270 248.70 466.31 0 [1,0]<stdout>: 8589934592 2147483648 float sum -1 33611 255.57 479.19 0 34474 249.17 467.20 0 [1,0]<stdout>: 17179869184 4294967296 float sum -1 66988 256.46 480.87 0 66717 257.50 482.82 0 [1,0]<stdout>:# Out of bounds values : 0 OK [1,0]<stdout>:# Avg bus bandwidth : 123.343Columns 9 and 13 in the output table show the in-place and out-of-place bus bandwidth calculated for the data size listed in column 2. In this case it is at maximum 444.31 and 444.41 GB/s respectively. Your actual results may be slightly different. The calculated average bus bandwidth is displayed at the end of the log. In this test run the average bus bandwidth was 79.9352 GB/s.
Lastly, delete the MPIJob:
Destroy¶
terraform destroy -target="module.eks_blueprints_addons" -auto-approve
terraform destroy -target="module.eks" -auto-approve
terraform destroy -auto-approve
See here for more details on cleaning up the resources created.